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61,005 resultsShowing papers similar to Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics
ClearMerging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics
Researchers developed an instrument combining holography, fluorescence, and machine learning for continuous, real-time characterization of airborne microplastics. The system can identify and classify microplastic particles in situ without requiring laboratory sample collection and analysis. The study represents an advance in monitoring technology that could improve understanding of atmospheric microplastic transport and human exposure.
Supplementary material to "Merging holography, fluorescence, and machine learning for in situ, continuous characterization and classification of airborne microplastics"
Researchers described the technical image analysis parameters used in a system that combines holography, fluorescence, and machine learning to identify and classify airborne microplastics in real time, providing the methodological detail needed to interpret particle shape and size measurements from the instrument.
A novel online method for the detection, analysis, and classification of airborne microplastics
Researchers developed an online method for real-time detection, analysis, and automated classification of airborne microplastics, enabling continuous monitoring of plastic particle concentrations and polymer types in ambient air without the time-consuming sample preparation required by conventional methods.
Microplastic pollution monitoring with holographic classification and deep learning
This study used digital holographic microscopy combined with deep learning to classify microplastic particles in water samples, achieving high classification accuracy and demonstrating the potential for automated, high-throughput microplastic monitoring.
Microplastic pollution assessment with digital holography and zero-shot learning
Researchers developed a digital holography system combined with zero-shot machine learning to identify and characterize microplastics in environmental samples without requiring labeled training data, offering a promising automated tool for large-scale microplastic monitoring.
Online in situ detection of atmospheric microplastics based on laser-induced breakdown spectroscopy
Researchers developed a laser-based detection system combined with machine learning that can identify and classify different types of microplastics in the air in real time. The system achieved high accuracy in distinguishing between common plastic types like polyethylene, polystyrene, and PVC. Better tools for monitoring airborne microplastics are important because people inhale these particles daily, and understanding what types are present in the air is the first step toward assessing respiratory health risks.
Digital holographic approaches to the detection and characterization of microplastics in water environments
This review examines advances in using digital holography as a high-throughput tool for detecting and characterizing microplastics in water. Researchers discuss both the hardware and software developments, including the growing role of artificial intelligence for classification tasks. The study highlights the emergence of field-portable holographic flow cytometers as a promising technology for real-time water monitoring of microplastic contamination.
Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning
Researchers used digital holographic microscopy combined with deep learning to detect and characterize microplastic particles in heterogeneous samples containing algae, microorganisms, and other natural particles. This automated approach could improve the speed and accuracy of environmental microplastic monitoring.
Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
Researchers developed a machine learning system that uses holographic 3D images to automatically classify microplastics in water samples, distinguishing them from other microscopic particles with high precision. Current microplastic monitoring is slow and labor-intensive, so automated detection tools are essential for large-scale environmental surveillance. This approach could significantly speed up the monitoring of microplastic pollution in aquatic environments.
Microplastic Identification via Holographic Imaging and Machine Learning
Researchers combined holographic imaging with machine learning algorithms to automatically identify and classify microplastics in water samples, achieving accurate particle detection without manual microscopy. This automated approach could significantly speed up microplastic monitoring in environmental samples.
Holographic Classifier: Deep Learning in Digital Holography for Automatic Micro-objects Classification
Researchers developed a deep learning system using digital holography to automatically classify micro-objects such as microplastics and pollutant particles without manual image processing. The system achieved fast, accurate identification, offering a promising automated tool for environmental pollution monitoring.
A fluorescence approach for an online measurement technique of atmospheric microplastics
Researchers developed a fluorescence-based approach for online, real-time detection of individual atmospheric microplastic particles, addressing the current gap in monitoring sources, transport, and abundance of airborne MPs.
Polarization Holographic Imaging for High-throughput Microplastic Analysis
Researchers developed a polarization holography system integrated with deep learning for high-throughput microplastic detection and analysis in aqueous environments. The system enables dynamic, real-time multimodal monitoring of microplastics by leveraging polarization contrast to distinguish particles in liquid samples.
Smart polarization and spectroscopic holography for real-time microplastics identification
Researchers developed a new optical imaging system called SPLASH that simultaneously captures polarization, holographic, and texture data from tiny particles — without needing a traditional spectrometer — and used machine learning to identify different types of microplastics with high accuracy. This approach could enable faster, more practical real-time monitoring of microplastic pollution in water.
Compact holographic microscope for imaging flowing microplastics
Researchers developed a compact holographic microscope capable of imaging flowing microplastics in aquatic environments, providing a fast, quantitative method for real-time characterization of plastic particle size and shape distributions.
Material analysis with polarization holography and machine learning
Researchers developed a polarization holographic imaging system combined with machine learning to identify different materials, demonstrating the approach on microplastic identification. This novel optical method could become a fast, non-destructive tool for classifying microplastics in environmental samples.
A fluorescence approach for an online measurement technique of atmospheric microplastics
Scientists developed a fluorescence-based instrument that can detect airborne microplastic particles in real time, rather than requiring slow laboratory analysis. The tool successfully identified common plastic types like PET, polyethylene, and polypropylene as individual particles in the air. This technology could help researchers better understand how much microplastic people are actually breathing in, which is important for assessing respiratory health risks from airborne plastic pollution.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.
Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection
Researchers developed an artificial intelligence-powered holographic microscopy system that can detect and classify nanoplastics in water in real time, without any sample preparation. The technology identified particles as small as 135 nanometers and tracked their movement in three dimensions. This represents a significant advancement in environmental monitoring, as previous methods required extensive lab processing to detect plastic particles this small.
Automatic Detection of Microplastics by Deep Learning Enabled Digital Holography
Researchers developed a digital holography system combined with deep learning to automatically detect and identify microplastics in water without manual image analysis. The system processes raw holographic images directly, offering a faster and more scalable approach to microplastic monitoring in environmental samples.
On the use of machine learning for microplastic identification from holographic phase-contrast signatures
This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.
Deep Classification of Microplastics Through Image Fusion Techniques
Deep neural networks were applied to classify microplastic fibers captured via digital holography microscopy, using image fusion techniques on the Holography Micro-Plastic Dataset benchmark. The study demonstrated promising accuracy for distinguishing microplastics from other debris, advancing automated microplastic identification in water quality monitoring.
Intelligent Digital Holographic systems to counteract microplastic pollution in marine waters
Researchers developed a digital holography system capable of detecting and classifying microplastic particles in seawater in a label-free, high-throughput manner. The system can identify plastic particles that are otherwise invisible to the naked eye and can be adapted for use with microfluidic devices. This technology offers a faster and more compact alternative to traditional microscopy methods for marine microplastic monitoring.
Holographic and polarization features analysis for microplastics characterization and water monitoring
Researchers explored digital holography and polarization imaging as a combined technique for characterizing and classifying microplastics in water, computing features including angle of polarization (AoP) and degree of linear polarization (DoLP) to distinguish microplastics from biological and natural particles. The method demonstrated potential for real-time, non-contact, in situ microplastic detection and water quality monitoring.